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Real-time code analysis and feedback
Course Description
Learning Objectives
This course teaches participants the following skills:
- Frame a business use case as a machine learning problem
- Create machine learning datasets that are capable of achieving generalization
- Implement machine learning models using TensorFlow
- Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization
- Build and operationalize distributed TensorFlow models
- Represent and transform features
Certification Curriculum
How Google Does Machine Learning
- What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently: it’s about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important not to skip the phases. We end with a recognition of the biases that machine learning can amplify and how to recognize them. Course Objectives:
Develop a data strategy around machine learning
Examine use cases that are then reimagined through an ML lens
Recognize biases that ML can amplify
Leverage Google Cloud Platform tools and environment to do ML
Learn from Google’s experience to avoid common pitfalls
Carry out data science tasks in online collaborative notebooks
Invoke pre-trained ML models from Cloud Datalab
Launching into Machine Learning
- Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation. Course Objectives:
Identify why deep learning is currently popular
Optimize and evaluate models using loss functions and performance metrics
Mitigate common problems that arise in machine learning
Create repeatable and scalable training, evaluation, and test datasets
Intro to TensorFlow
- We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives:
Create machine learning models in TensorFlow
Use the TensorFlow libraries to solve numerical problems
Troubleshoot and debug common TensorFlow code pitfalls
Use tf.keras and tf_estimator to create, train, and evaluate ML models
Train, deploy, and productionalize ML models at scale with Cloud ML Engine
Feature Engineering
- A key component of building effective machine learning models is to convert raw data to features in a way that allows ML to learn important characteristics from the data. We discuss how to represent features and code this up in TensorFlow. Human insight can be brought to bear in machine learning problems through the use of custom feature transformations. In this module, we talk about common types of transformations and how to implement them at scale.
Turn raw data into feature vectors
Preprocess and create new feature pipelines with Cloud Dataflow
Create and implement feature crosses and assess their impact
Write TensorFlow Transform code for feature engineering
The Art and Science of ML
- Machine Learning is both an art that involves knowledge of the right mix of parameters that yields accurate, generalized models and a science that involves knowledge of the theory to solve specific types of ML problems. We discuss regularization, dealing with sparsity, multi-class neural networks, reusable embeddings, and many other essential concepts and principles.
Optimize model performance with hyperparameter tuning
Experiment with neural networks and fine-tune performance
Enhance ML model features with embedding layers
Create reusable custom model code with the Custom Estimator
Prerequisites
To get the most out of this specialization, participants should have:
- Experience coding in Python
- Knowledge of basic statistics
- Knowledge of SQL and cloud computing
Download Brochure
Join Machine Learning Training with TensorFlow on Google Cloud Platform and gain the knowledge and skills needed to land an entry-level job in Machine Learning. A Path to In-Demand Jobs.
Certification Assessment
When you complete the course in the machine learning training program, you’ll earn a Certificate to share with your professional network as well as unlock access to career support resources to help you kickstart your new career. Many Professional Certificates have hiring partners that recognize the Professional Certificate credential and others can help prepare you for a certification exam. You can find more information on individual Professional Certificate pages where it applies.
- High Success rate
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- Training from Recognized Trainer
- Post-workshop support by the Coaches
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Funding & Price Chart
| Individual / Company Sponsored (Banking and Finance) |
Company Sponsored (Non Banking and Finance) |
||
| Singaporeans age 40 and above (70% funding) | Singaporeans age below 40 and all PRs (50% funding) | (No Funding) | |
| Full Course Fee before GST | $3,500.00 | $3,500.00 | $3,500.00 |
| 9% GST | $315.00 | $315.00 | $315.00 |
| Total Course Fee | $3,815.00 | $3,815.00 | $3,815.00 |
| Less: IBF Funding | $2,670.50 | $1,907.50 | NA |
| Net Fee Payable | $1,144.50 | $1,907.50 | $3,815.00 |
Frequently Asked Questions
Training FAQs
In the Machine Learning Training, you will learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models and offer high-performance predictions
The duration of Machine Learning Training is two-days.
Data Engineers and programmers interested in learning how to apply machine learning in practice or anyone interested in learning how to build and operationalize TensorFlow models can apply for Machine Learning Training.
With Machine Learning Training with TensorFlow Certification, you can strengthen your cloud knowledge, earn a digital certificate, and start preparing for an industry-recognized Google Cloud certification.
Participants should have experience coding in Python, knowledge of basic statistics, and knowledge of SQL and cloud computing.
IBF Accredited Programmes
The IBF is an independent quality assurance framework benchmarked under industry standards to get the finance practitioners future-ready by strengthening their financial capability and industrial skills.
The IBF financial training scheme provides funding for training and assessment programmes specifically to enhance the underlying competencies of the workforce in the financial/tech sector.
Training allowance grant supports banking, financial services and insurance organizations to provide industry-standard training with a focus to upgrade the skills of their employees.
You can avail of more information about the IBF course and training programs here.
The financial Institutions and fin-tech firms regulated by the Authority of Singapore can avail the benefit of course fee subsidy.
Singapore residents and permanent residents can claim upto 90% subsidy. Participants need to pay only 5% + GST to attend the training.
UTAP Funding
The UTAP (Union Training Assistance Program) is a benefit provided to NTUC members for encouraging professional upskilling at a minimal cost.
Singapore citizens and permanent residents can avail of UTAP support who are NTUC members.
UTAP provides upto 50% support of the course fee. It provides $250 for participants under 39 years and $500 for participants above 40 years.
Login to NTUC to submit your application to claim UTAP support. You can also reach out to us @ for further support.
UTAP funding can be claimed after the completion of the course only.
SkillsFuture Credit
SkillsFuture Credit is an initiative by the Singapore government to defray the training costs to encourage upgradation of skills specifically for finance/tech professions.
All Singaporeans aged 25 and above can avail of the benefit of SkillsFuture Credit. For more information, click here.
No, the small/medium sized enterprises cannot apply for SkillsFuture Credit since it is provided only to individuals.
No, SkillsFuture is not a cash account, it can be used only to offset course fees of completed training courses approved by SkillsFuture. Therefore you do not earn any interest from it.
The course fee of the training program is directly paid to the course provider through MySkillsFuture and you can avail directly from them.
You can login to MySkillsFuture, select the course and enter the details, enter course fee to be paid including GST and put the amount to be claimed. Upload invoice and click submit.
Professional Machine Learning Engineer Training
Spike your career upwards with Professional Machine Learning Engineer Certification and become relevant to high-paying jobs. Machine learning is a skill of the future, and a Machine learning certification will only make you more marketable while expanding your career prospects tenfold. With Machine Learning with TensorFlow Certification on Agilitics, you can strengthen your cloud knowledge, earn a digital certificate, and start preparing for an industry-recognized Google Cloud certification.
With upto 90% subsidies for citizens above 40 years, and 80% subsidies for citizens below 40 years and all Permanent Residents, learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform.
The American billionaire entrepreneur, Mark Cuban said, “Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.” Such is the importance of machine learning, hence the Professional Machine Learning Certification will take your career to the next level, enhancing your pay scale and value in the market. With all vertical sectors gradually incorporating machine learning into their work structure, the demand for professionals who can drive valuable algorithms from machine learning has increased greatly. This five-day workshop aims at boosting your cloud career as you learn the fundamentals of machine learning and equip yourself with the skills and tools to help your organization drive insights from data and process complex data sets using machine learning.
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